Modeling Wind Risk for Commercial Properties

Commercial property presents quite a challenge for predictive modelers: How can we rely on models that implicitly assume some degree of homogeneity among risks when we’re dealing with a heterogeneous risk pool of insured locations? For commercial property, the risks range from fruit stands to high-rise office buildings to chemical manufacturing plants.

But here’s the good news: ISO’s Enhanced Wind Rating Program has met that challenge. As a starting point, the program determines a building-specific loss cost using the current manual loss cost — which varies by state, territory, and “wind symbol” (itself a function of factors such as construction and building height). The program will eventually apply a series of credits and debits based on approximately 40 risk characteristics that ISO is collecting during building inspections. In the meantime, we’ve developed and filed an interim program that uses 7 basic risk characteristics.

In the typical modeling exercise, you collect information and run a series of generalized linear models (GLMs) to obtain risk relativities. For instance, private passenger automobile uses variables such as age, gender, marital status, and vehicle type. For commercial property, there are two important differences: Catastrophes are a significant factor, particularly for the wind peril. And commercial buildings vary tremendously, in contrast to drivers, who are relatively homogeneous.

To address those concerns, the Enhanced Wind Rating Program combines three sources of knowledge: weather-related models produced by AIR Worldwide, engineering information from ISO’s on-site building inspections, and insurance data from ISO’s premium and loss databases. Think of it as a composite approach to predictive modeling.

What have we learned? First, it’s important to identify and explore areas of poor model fit. Second, it’s smart to communicate with subject matter experts. Both lessons derive from an interesting discovery we made: Buildings with three particular risk characteristics had considerably worse loss experience than if you considered each characteristic in isolation.

Statisticians know that significant three-way interactions are rare, and even when they do exist, they’re very difficult to identify. As an actuary with little intuition for what characteristics cause a building to be susceptible to wind losses (other than obvious things such as construction), I was surprised to see that this interaction term was significant. But we contacted an experienced commercial property inspector who was not surprised at all — and was able to supply a sensible engineering reason for the phenomenon.

The lesson we learned: Zero in on where your model fits poorly. After determining that there's no problem with the data, ask the professionals in the field what makes those kinds of risks different. They’ll often provide the sensible advice the model could only hint at.